Automatic feature recognition in image processing has a large number of applications, and has been extensively investigated for some time. For example, as medical imaging techniques become more widely used, there is a corresponding need for automatic analysis of medical image data. This need is most pressing in applications which generate large amounts of data for analysis. For example, Computed Tomographic Colonoscopy (CTC) can provide thousands of CT images, each of which needs interpretation by a radiologist to locate polyps or other abnormal features. Colon polyps are small, inward-facing protrusions on the colon surface, and are considered to be precursors to colon cancer. Accordingly, methods for providing automatic detection of polyp features in CTC data have been investigated.
One approach is based on 3-D pattern recognition, where a system identifies a candidate location for analysis, then computes a probability measure for the candidate location to be a polyp. The computation of the probability measure is based on prior training of the system with a training set of known polyps. Such an approach is considered in US2006/0079761. However, most approaches for automatic polyp detection rely on computation of surface curvature information from the given 3-D data, which can be done in various ways.
For example, in U.S. Pat. No. 7,043,064, polyps are identified by computing surface normals and looking for intersections (or overlaps) of the surface normals. This surface normal overlap (SNO) method is based on the observation that surface normals from a polyp will tend to overlap or intersect, while surface normals from non-polyp features (e.g., haustral folds) do not tend to overlap or intersect.
Another approach for determining curvature is based on computing curvatures directly from 3-D image data (e.g., as described in US 2005/0196041, US 2005/0111713, and U.S. Pat. No. 6,345,112. In such approaches, voxels in the 3-D image belonging to the surface are identified by a surface locating algorithm, then the curvatures at the surface voxels are computed directly from the 3-D data.
Yet another approach for determining curvature is surface patch fitting. In these approaches, a surface is identified in the 3-D data, the surface is divided into patches, and a simple functional fit is performed to each surface patch. The curvature for each patch is determined from the fitted function for that patch. Surface patch fitting is considered in U.S. Pat. No. 6,345,112, PCT application PCT/US00/05596, and in an article by Huang et al. entitled “Surface Curvature estimation for automatic colonic polyp detection”, Proc. SPIE 5746, Medical Imaging 2005, ed. Amini and Manduca, pp. 393-402, 2005).
For all of these approaches, some form of smoothing is typically required to obtain useful results in practice, since raw CT data tends to be noisy, and computing curvature from CT data tends to amplify noise because the curvature is a second derivative of the data. However, such smoothing can undesirably introduce errors and anomalies into the reported curvature results. For example, smoothing the 3-D image can result in the curvature estimates being corrupted by contributions from nearby structures in the data that are irrelevant.
Accordingly, it would be an advance in the art to provide surface feature recognition for 3-D image data having improved performance and reduced corruption during curvature computation.